R Markdown networkD3

libraries etc.

library(tidyverse)
library(ggtext)

library(networkD3)
library(d3Network)
library(igraph)
library(plotly)

plotly example

sqa_top5s <- read_csv("sqa_top5s.csv")

## 
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────
## cols(
##   year = col_double(),
##   Subject = col_character(),
##   gender = col_character(),
##   NoOfStudents = col_double(),
##   popularity = col_double(),
##   popularityOverTime = col_double(),
##   Subject.label = col_character()
## )

time_range <- sqa_top5s %>%
    select(year) %>%
    n_distinct()

options(repr.plot.width = 18, repr.plot.height = 9)

plot1 <- sqa_top5s %>%
    highlight_key(~gender) %>%

    ggplot(aes(y = fct_reorder(Subject.label, popularity), x = popularityOverTime,
               text = paste(gender, " - choose", Subject, round(popularityOverTime * 100), "% of the time"),
               mode = "markers+text")) +
        geom_segment(aes(xend = 0, yend = Subject.label), alpha = 0.45) +
        geom_point(aes(colour = gender, shape = gender), size = 3) +
        scale_shape_manual(values = c(6, 2)) + #-0x2640L, -0x2642L)) + # \u2640 and \u2642
        guides(size = FALSE, alpha = FALSE) +
        scale_x_continuous(labels = function(x) scales::percent(abs(x)), breaks = seq(0, 1, 0.2)) +
        ylab(NULL) + xlab("Frequency - in Top 5 Subjects Over Time (Excluding English & Maths)") +
        theme(axis.text.y = element_markdown(size = 14),
              axis.text.x = element_text(size = 12, angle = 45, vjust = 0.5),
              #text = element_text(family = "Helvetica")
        ) #%>%
        #suppressMessages() %>%
        #suppressWarnings() - ignored ...

plot1

ggplotly(plot1, tooltip = "text", dynamicTicks = "x", width = 1800, height = 700,
         margin = list(l = 0, r = 5, b = 5, t = 5, pad = 2),
         yais = yaxis <- list(automargin = FALSE, margin = list(l = 0, r = 5, b = 5, t = 5, pad = 2))) %>%

            style(hoveron = "points", hoverinfo = "text", hoverlabel = list(bgcolor = "white")) %>%
            highlight(on = "plotly_hover", off = "plotly_doubleclick") %>%
            rangeslider()

plotly update example

library(quantmod)
quantmod::getSymbols("AAPL")

## [1] "AAPL"

df <- data.frame(Date = index(AAPL), coredata(AAPL))

high_annotations <- list(
  x = df$Date[df$AAPL.High == max(df$AAPL.High)],
  y = max(df$AAPL.High),
  xref = 'x', yref = 'y',
  text = paste0('High: $',max(df$AAPL.High)),
  ax = 0, ay = -40
)

low_annotations <- list(
  x = df$Date[df$AAPL.Low == min(df$AAPL.Low)],
  y = min(df$AAPL.Low),
  xref = 'x', yref = 'y',
  text = paste0('Low: $',min(df$AAPL.Low)),
  ax = 0, ay = 40
)


# updatemenus component
updatemenus <- list(
  list(
    active = -1,
    type= 'buttons',
    buttons = list(
      list(
        label = "High",
        method = "update",
        args = list(list(visible = c(FALSE, TRUE)),
                    list(title = "Apple High",
                         annotations = list(c(), high_annotations)))),
      list(
        label = "Low",
        method = "update",
        args = list(list(visible = c(TRUE, FALSE)),
                    list(title = "Apple Low",
                         annotations = list(low_annotations, c() )))),
      list(
        label = "Both",
        method = "update",
        args = list(list(visible = c(TRUE, TRUE)),
                    list(title = "Apple",
                         annotations = list(low_annotations, high_annotations)))),
      list(
        label = "Reset",
        method = "update",
        args = list(list(visible = c(TRUE, TRUE)),
                    list(title = "Apple",
                         annotations = list(c(), c())))))
  )
)

df %>%
    plot_ly(type = 'scatter', mode = 'lines') %>%
        add_lines(x = ~Date, y = ~AAPL.High, name = "High", line = list(color = "#33CFA5")) %>%
        add_lines(x = ~Date, y = ~AAPL.Low, name = "Low", line = list(color = "#F06A6A")) %>%

        layout(title = "Apple", showlegend = FALSE,
               xaxis = list(title = "Date"),
               yaxis = list(title = "Price ($)"),
               updatemenus = updatemenus)

mtcars %>%
    highlight_key(~cyl) %>%

    plot_ly(
        x = ~wt, y = ~mpg, text = ~cyl, mode = "markers+text",
        textposition = "top", hoverinfo = "x+y"
    ) %>%

    highlight(on = "plotly_hover", off = "plotly_doubleclick")

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

igraph example

data(MisLinks)
data(MisNodes)

forceNetwork(Links = MisLinks, Nodes = MisNodes, Source = "source",
             Target = "target", Value = "value", NodeID = "name",
             #Nodesize = "size",
             radiusCalculation = "Math.sqrt(d.nodesize) + 6",
             Group = "group", fontSize = 16, opacity = 1, legend = T, bounded = T)

igraph example

# Make a correlation matrix:
mat <- cor(t(mtcars[,c(1,3:6)]))

# Keep only high correlations
mat[mat<0.995] <- 0
 
# Make an Igraph object from this matrix:
network <- graph_from_adjacency_matrix( mat, weighted=T, mode="undirected", diag=F)

plot(network)

igraph to networkD3 example

# Use igraph to make the graph and find membership
karate <- make_graph("Zachary")
wc <- cluster_walktrap(karate)
members <- membership(wc)

# Convert to object suitable for networkD3
karate_d3 <- igraph_to_networkD3(karate, group = members)

# Create force directed network plot
forceNetwork(Links = karate_d3$links, Nodes = karate_d3$nodes,
             Source = 'source', Target = 'target',
             NodeID = 'name', Group = 'group')

networkD3 example

data <- data_frame(
  from = c("A", "A", "B", "D", "C", "D", "E", "B", "C", "D", "K", "A", "M"),
  to = c("B", "E", "F", "A", "C", "A", "B", "Z", "A", "C", "A", "B", "K")
)

# Plot
p <- simpleNetwork(data, height="100px", width="100px",
        Source = 1,                 # column number of source
        Target = 2,                 # column number of target
        linkDistance = 10,          # distance between node. Increase this value to have more space between nodes
        charge = -900,                # numeric value indicating either the strength of the node repulsion (negative value) or attraction (positive value)
        fontSize = 14,               # size of the node names
        fontFamily = "serif",       # font og node names
        linkColour = "#666",        # colour of edges, MUST be a common colour for the whole graph
        nodeColour = "#69b3a2",     # colour of nodes, MUST be a common colour for the whole graph
        opacity = 0.9,              # opacity of nodes. 0=transparent. 1=no transparency
        zoom = T                    # Can you zoom on the figure?
        )

simpleNetwork

src <- c("A", "A", "A", "A", "B", "B", "C", "C", "D")
target <- c("B", "C", "D", "J", "E", "F", "G", "H", "I")
networkData <- data.frame(src, target)
simpleNetwork(networkData)

forceNetwork

data(MisLinks)
data(MisNodes)
forceNetwork(Links = MisLinks, Nodes = MisNodes, Source = "source",
             Target = "target", Value = "value", NodeID = "name",
             Group = "group", opacity = 0.4)

sankeyNetwork

URL <- "https://raw.githubusercontent.com/christophergandrud/d3Network/sankey/JSONdata/energy.json"
Energy <- RCurl::getURL(URL, ssl.verifypeer = FALSE)
EngLinks <- JSONtoDF(jsonStr = Energy, array = "links")
EngNodes <- JSONtoDF(jsonStr = Energy, array = "nodes")

sankeyNetwork(Links = EngLinks, Nodes = EngNodes, Source = "source",
              Target = "target", Value = "value", NodeID = "name",
              fontSize = 12, nodeWidth = 30)

diagonalNetwork

Flare <- RCurl::getURL("https://gist.githubusercontent.com/mbostock/4063550/raw/a05a94858375bd0ae023f6950a2b13fac5127637/flare.json")
Flare <- rjson::fromJSON(Flare)
diagonalNetwork(List = Flare, fontSize = 10, opacity = 0.9, margin=0)

radialNetwork

Flare <- RCurl::getURL("https://gist.githubusercontent.com/mbostock/4063550/raw/a05a94858375bd0ae023f6950a2b13fac5127637/flare.json")
Flare <- rjson::fromJSON(Flare)
radialNetwork(List = Flare, fontSize = 10, opacity = 0.9, margin=0)

dendroNetwork

hc <- hclust(dist(USArrests), "ave")
dendroNetwork(hc, height = 600)